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Found 10 papers, 2 papers with code
Learning Efficient Representations for Keyword Spotting with Triplet Loss
In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
MediaSpeech: Multilanguage ASR Benchmark and Dataset
The performance of automated speech recognition (ASR) systems is well known to differ for varied application domains.
Ranked #1 on
Speech Recognition
on MediaSpeech
Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions
This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech.
Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions
In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results in low-resource setting for the language identification task and set up a SOTA for the Low Resource ASR challenge dataset.
Language ID Prediction from Speech Using Self-Attentive Pooling
This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech.
Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants
Kiosks are a popular self-service option in many fast-food restaurants, they save time for the visitors and save labor for the fast-food chains.
Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen
We opensource under CC BY 4. 0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset.
On Unsupervised Training of Link Grammar Based Language Models
In this short note we explore what is needed for the unsupervised training of graph language models based on link grammars.
Autocorrelations Decay in Texts and Applicability Limits of Language Models
We show that the laws of autocorrelations decay in texts are closely related to applicability limits of language models.
Long Text Generation Challenge
We propose a shared task of human-like long text generation, LTG Challenge, that asks models to output a consistent human-like long text (a Harry Potter generic audience fanfic in English), given a prompt of about 1000 tokens.
